Upscaling river networks for use in climate models

Upscaling river networks for use in climate models

[1] Upscaling fine resolution river networks in a realistic manner is a cumbersome process and manual corrections are difficult to avoid. A modified algorithm is presented that offers improvement over the existing approaches and requires comparatively fewer manual corrections. The algorithm uses fine resolution flow directions to find the adjacent coarse resolution grid cell in which the majori...

Automated Upscaling of River Networks for Macroscale Hydrological Modeling

Prediction of Zarrinehrud River Run-Off in the Climate Change Condition using Artificial Neural Networks

   In the present research, the climate change effect on variation of surface runoff of Zarrinehrud located in the Miandoab plain was investigated. In this direction, the scenarios including A1B, A2 and B1 via LARS-WG downscaling model and with applying the HadCM3 general circulation model and artificial neural network model in two different periods (2046-2065, 2080 -2099) were studied. For thi...

Flexible regression models over river networks

Many statistical models are available for spatial data but the vast majority of these assume that spatial separation can be measured by Euclidean distance. Data which are collected over river networks constitute a notable and commonly occurring exception, where distance must be measured along complex paths and, in addition, account must be taken of the relative flows of water into and out of co...

Upscaling river biomass using dimensional analysis and hydrogeomorphic scaling

[1] We propose a methodology for upscaling biomass in a river using a combination of dimensional analysis and hydro-geomorphologic scaling laws. We first demonstrate the use of dimensional analysis for determining local scaling relationships between Nostoc biomass and hydrologic and geomorphic variables. We then combine these relationships with hydraulic geometry and streamflow scaling in order...